Physical Sciences and Mathematics Commons^™

Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu

Dissertations

During the past decade, drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug­abuse risk behavior at a population scale, such as among the population of Twitter users, can help to monitor the trend of drug­abuse incidents. However, traditional methods do not effectively detect drug­abuse risk behavior in tweets, mainly due to the sparsity of such tweets and the noisy nature of tweets. In the first part of this dissertation work, the task of classifying tweets as containing drug­abuse risk behavior or not, is studied. Millions of public …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

A Novel Arabic Corpus For Text Classification Using Deep Learning And Word Embedding, Roua A. Abou Khachfeh, Islam El Kabani, Ziad Osman

BAU Journal - Science and Technology

Over the last years, Natural Language Processing (NLP) for Arabic language has obtained increasing importance due to the massive textual information available online in an unstructured text format, and its capability in facilitating and making information retrieval easier. One of the widely used NLP task is “Text Classification”. Its goal is to employ machine learning technics to automatically classify the text documents into one or more predefined categories. An important step in machine learning is to find suitable and large data for training and testing an algorithm. Moreover, Deep Learning (DL), the trending machine learning research, requires a lot of …

Explaining Deep Learning Models For Tabular Data Using Layer-Wise Relevance Propagation, Ihsan Ullah, Andre Rios, Vaibhov Gala, Susan Mckeever

Articles

Trust and credibility in machine learning models are bolstered by the ability of a model to explain its decisions. While explainability of deep learning models is a well-known challenge, a further challenge is clarity of the explanation itself for relevant stakeholders of the model. Layer-wise Relevance Propagation (LRP), an established explainability technique developed for deep models in computer vision, provides intuitive human-readable heat maps of input images. We present the novel application of LRP with tabular datasets containing mixed data (categorical and numerical) using a deep neural network (1D-CNN), for Credit Card Fraud detection and Telecom Customer Churn prediction use …

Eating Detection With A Head-Mounted Video Camera, Shengjie Bi, David Kotz

Computer Science Technical Reports

In this paper, we present a computer-vision based approach to detect eating. Specifically, our goal is to develop a wearable system that is effective and robust enough to automatically detect when people eat, and for how long. We collected video from a cap-mounted camera on 10 participants for about 55 hours in free-living conditions. We evaluated performance of eating detection with four different Convolutional Neural Network (CNN) models. The best model achieved accuracy 90.9% and F1 score 78.7% for eating detection with a 1-minute resolution. We also discuss the resources needed to deploy a 3D CNN model in wearable or …

The Detection Of Sexual Harassment And Chat Predators Using Artificial Neural Network, Noor Amer Hamzah, Ban N. Dhannoon

Karbala International Journal of Modern Science

The vast increase in using social media sites like Twitter and Facebook led to frequent sexual_harassment on the Internet, which is considered a major societal problem. This paper aims to detect sexual_harassment and cyber_predators in early phase. We used deeplearning like Bidirectionally-long-short-term memory. Word representations are carefully reviewed in text specific to mapping to real number vectors. The chat sexual predators Detection_approach with the proposed_model. The best results obtained by the performance measured with F0.5-score were the result is_0.927 with proposed_models. The accuracy measured is_97.27% in the proposed_model. The comments sexual_harassment Detection_approach the result is_0.925 F0.5-score, and accuracy measured is_99.12%.

Pranayama Breathing Detection With Deep Learning, Bikash Shrestha

Theses

Yoga, a complementary health approach, according to a 2017 National Health Interview Survey by the Center for Disease Control and Prevention (CDC), is a choice of around 14.3% adults in the US. Kapalbhati pranayama, a yoga practice of alternating fast exhales and longer passive inhales, is understood to improve our health. Incorrect and irregular practices, however, can cause injuries and adverse effects. To avoid these undesired effects, it is essential to maintain a pace fit for the practitioner. In the absence of any tools to observe a pace of practice, this work develops a deep learning method that listens to …

Auto-Curation Of Large Evolving Image Datasets, Sara Mousavicheshmehkaboodi

Doctoral Dissertations

Large image collections are becoming common in many fields and offer tantalizing opportunities to transform how research, work, and education are conducted if the information and associated insights could be extracted from them. However, major obstacles to this vision exist. First, image datasets with associated metadata contain errors and need to be cleaned and organized to be easily explored and utilized. Second, such collections typically lack the necessary context or may have missing attributes that need to be recovered. Third, such datasets are domain-specific and require human expert involvement to make the right interpretation of the image content. Fourth, the …

From Language Comprehension Towards General Ai, Binay Dahal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Language comprehension or more formally, natural language understanding is one of the major undertakings in Artificial Intelligence. In this work, we explore a few of the problems in language understanding using fixed deep learning models. Specifically, first, we look into question generation. Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, making progress in question generation is significant. We introduce a novel task called “question generation with masked target answer” and propose various models and present the baseline result for the task. Next, we extend on the question generation task and develop a …

Models Versus Datasets: Reducing Bias Through Building A Comprehensive Ids Benchmark, Rasheed Ahmad, Izzat Alsmadi, Wasim Alhamdani, Lo'ai A. Tawalbeh

Computer Science Faculty Publications

Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these …

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

Joint Linear And Nonlinear Computation With Data Encryption For Efficient Privacy-Preserving Deep Learning, Qiao Zhang

Electrical & Computer Engineering Theses & Dissertations

Deep Learning (DL) has shown unrivalled performance in many applications such as image classification, speech recognition, anomalous detection, and business analytics. While end users and enterprises own enormous data, DL talents and computing power are mostly gathered in technology giants having cloud servers. Thus, data owners, i.e., the clients, are motivated to outsource their data, along with computationally-intensive tasks, to the server in order to leverage the server’s abundant computation resources and DL talents for developing cost-effective DL solutions. However, trust is required between the server and the client to finish the computation tasks (e.g., conducting inference for the newly-input …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Deep Learning Predicts Ebv Status In Gastric Cancer Based On Spatial Patterns Of Lymphocyte Infiltration, Baoyi Zhang, Kevin Yao, Min Xu, Jia Wu, Chao Cheng

Computer Vision Faculty Publications

EBV infection occurs in around 10% of gastric cancer cases and represents a distinct subtype, characterized by a unique mutation profile, hypermethylation, and overexpression of PD-L1. Moreover, EBV positive gastric cancer tends to have higher immune infiltration and a better prognosis. EBV infection status in gastric cancer is most commonly determined using PCR and in situ hybridization, but such a method requires good nucleic acid preservation. Detection of EBV status with histopathology images may complement PCR and in situ hybridization as a first step of EBV infection assessment. Here, we developed a deep learning-based algorithm to directly predict EBV infection …

Treatment Selection Using Prototyping In Latent-Space With Application To Depression Treatment, Akiva Kleinerman, Ariel Rosenfeld, David Benrimoh, Robert Fratila, Caitrin Armstrong, Joseph Mehltretter, Eliyahu Shneider, Amit Yaniv-Rosenfeld, Jordan Karp, Charles F. Reynolds, Gustavo Turecki, Adam Kapelner

Publications and Research

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not …

Deep Learning For Multiclass Classification, Predictive Modeling And Segmentation Of Disease Prone Regions In Alzheimer’S Disease, Maryamossadat Aghili

FIU Electronic Theses and Dissertations

One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s Disease (AD) is identifying the subtle changes that define the early onset of the disease. This dissertation investigates three of the main challenges confronted when such subtle changes are to be identified in the most meaningful way. These are (1) the missing data challenge, (2) longitudinal modeling of disease progression, and (3) the segmentation and volumetric calculation of disease-prone brain areas in medical images. The scarcity of sufficient data compounded by the missing data challenge in many longitudinal samples exacerbates the problem as we seek statistical meaningfulness in multiclass …

Patchnet: Hierarchical Deep Learning-Based Stable Patch Identification For The Linux Kernel, Thong Hoang, Julia Lawall, Yuan Tian, Richard J. Oentaryo, David Lo

Research Collection School Of Computing and Information Systems

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical …

Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate …

Multi-Object Localization In Robotic Hand, Tsing Tsow

USF Tampa Graduate Theses and Dissertations

We have developed a machine learning approach to localized objects inside a robotic hand using only images from 2D cameras. Specifically, we used deep learning method (You Only Look Once, YOLO) and Iterative closest Point (ICP) to estimate the 3D coordinates of the objects in a robotic hand. This method will also output the number of objects inside the robotic hand in addition to the coordinates of the objects. We have demonstrated the performance with simulation and obtained typical accuracy within a few pixels (couple mm) and counting accuracy of about 76%. We have also applied it to real images, …

Detection Of Dental Apical Lesions Using Cnns On Periapical Radiograph, Chun-Wei Li, Szu-Yin Lin, He-Sheng Chou, Tsung-Yi Chen, Yu-An Chen, Sheng-Yu Liu, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo

Department of Information Systems & Computer Science Faculty Publications

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or …

Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, Yet Effective Time Series Cnn-Based Approach, Hossein Sayadi, Yifeng Gao, Hosein Mohammadi Makrani, Jessica Lin, Paulo Cesar Costa, Setareh Rafatirad, Houman Homayoun

Computer Science Faculty Publications and Presentations

According to recent security analysis reports, malicious software (a.k.a. malware) is rising at an alarming rate in numbers, complexity, and harmful purposes to compromise the security of modern computer systems. Recently, malware detection based on low-level hardware features (e.g., Hardware Performance Counters (HPCs) information) has emerged as an effective alternative solution to address the complexity and performance overheads of traditional software-based detection methods. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers during execution at run-time. Prior HMD methods though effective have limited their study on …

Deep Learning Models For Irregularly Sampled And Incomplete Time Series, Satya Narayan Shukla

Doctoral Dissertations

Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, geology, finance, and health. Such data present fundamental challenges to many classical models from machine learning and statistics. The first challenge with modeling such data is the presence of variable time gaps between the observation time points. The second challenge is that the dimensionality of the inputs can be different for different data cases. This occurs naturally due to the fact that different data cases are likely to include different numbers of observations. The third challenge is that different irregularly sampled instances have …

Research On The Network Of 3d Smoke Flow Super-Resolution Data Generation, Jinlian Du, Shufei Li, Xueyun Jin

Journal of System Simulation

Abstract: Aiming at the problem of low data generation efficiency due to the high complexity of solving the N-S equation of smoke flow field, a deep learning model which can generate high-resolution smoke flow data based on low-resolution smoke flow data solved by N-S equation is explored and designed. Based on the Generative Adversarial Network, the smoke data reconstruction network based on the sub voxel convolution layer is constructed. Considering the fluidity of smoke, time loss based on advection step is introduced into the loss function to realize high-precision smoke simulation. By extending the image super-resolution quality evaluation index, the …

Deep Learning For Image Super-Resolution: A Survey, Zhihao Wang, Jian Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by …

Traffic Sign And Light Detection Using Deep Learning For Automotive Applications, Humaira Naimi

Electronic Theses and Dissertations

Traffic sign and light detection are core components of Advanced Driver Assistance Systems (ADAS) and self-driving vehicles. The automotive industry is widely employing numerous approaches for automation through computer vision techniques. Object detection algorithms based on deep learning can be divided into two main categories, two stage and single stage detection algorithms. Two stage algorithms are designed to improve detection accuracy. While single stage algorithms are constructed to be faster, this increases their suitability for real time applications. This thesis presents a lightweight traffic sign and light detector by adapting a single stage, Single Shot Multibox Detection (SSD) algorithm by …

Neural Network Based Approach For Detecting Location Spoofing In Vehicular Communication, Smarth Kukreja

Electronic Theses and Dissertations

Vehicular Ad hoc Network (VANET) is an evolving subset of MANET. It's deployed on the roads, where vehicles act as mobile nodes. Active security and Intelligent Transportation System (ITS) are integral applications of VANET, which require stable and uninterrupted vehicle-to-vehicle communication technology. VANET, is a type of wireless network, due to which it is quite prone to security attacks. Extremely dynamic connections, sensitive data sharing and time-sensitivity of this network make it a vulnerable to security attacks. The messages shared between the vehicles are the basic safety message (BSM), these messages are broadcasted by each vehicle in the network to …

Towards More Trustworthy Deep Learning: Accurate, Resilient, And Explainable Countermeasures Against Adversarial Examples, Fei Zuo

Theses and Dissertations

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. Along with the prevalence of deep learning techniques, the threat of AEs attracts increasingly attentions since it may lead to serious consequences in some vital applications such as disease diagnosis.

To defeat attacks based on AEs, both detection and defensive techniques attract the research community’s attention. Given an input image, the detection system outputs whether it is an …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …

Artificial Intelligence In Cyber Security, University Of Maine Artificial Intelligence Initiative

General University of Maine Publications

UMaine AI draws top talent and leverages a distinctive set of capabilities from the University of Maine and other collaborating institutions from across Maine and beyond, while it also recruits world-class talent from across the nation and the world. It is centered at the University of Maine, leveraging the university’s strengths across disciplines, including computing and information sciences, engineering, health and life sciences, business, education, social sciences, and more.

Users’ Sentiment Analysis Toward National Digital Library Of India: A Quantitative Approach For Understanding User Perception, Ritu Sharma, Sarita Gulati, Amanpreet Kaur, Rupak Chakravarty

Library Philosophy and Practice (e-journal)

Sentiment analysis is also known as opinion mining. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. It is extremely used by business, educational organizations, and social media monitoring to gain the general outlook of the wide public regarding their product and policy. The current study looks for gaining insights into user reviews on the National Digital Library of India (NDLI) mobile app (android and iOS). For this purpose, sentiment analysis will be used. It yields an average of 3.64/5 ratings based on 11,861 reviews. The dataset includes a total of 4560 user …